Stop bleeding margin to GPUs and tokens.
We instrument every layer - model, prompt, retrieval, infrastructure - to make AI spend visible, attributable and controllable. Typical engagements cut LLM and GPU bills by 30–60% with no quality loss.
Three signals you're ready for this work.
"AI bill doubling quarterly, nobody can explain why."
Per-feature, per-tenant cost attribution in two weeks.
"Token spend untraceable to product P&L."
Showback dashboards tying every dollar to a feature and a tenant.
"GPU clusters at 20% utilization, leadership wants more anyway."
Right-sized inference fleet with utilization, batching and routing controls.
What clients see.
How we work.
Cost attribution wired across model, prompt, retrieval and infrastructure layers.
Token, model and GPU spend baselined. Optimization opportunities ranked.
Caching, batching, routing, model substitution, GPU right-sizing applied.
Showback, chargeback, alerts, quarterly cost reviews.
What you walk away with.
Concrete artefacts your board, your auditors and your delivery teams can all act on.
- Per-feature, per-tenant cost-attribution model
- Prompt, model and retrieval optimization playbook
- Caching, batching and routing implementation
- GPU and inference-cluster right-sizing recommendations
- Showback / chargeback dashboards and budget alerts
- Quarterly FinOps review cadence with named owners
European SaaS · LLM spend optimization.
Cut a $2.4M annual LLM bill by 47% in eight weeks with no measurable quality drop.
Asked and answered.
30 / 60 / 90.
Per-feature, per-tenant attribution live. Top spend drivers identified.
Caching, routing and model substitution applied. 20–30% saved on early scope.
30–60% reduction confirmed against baseline. Governance cadence live.
A delivery partner, not a deck factory.
Senior engineers do the optimization - not analysts producing recommendations.
Eval sets and regression gates protect every optimization.
Per-customer attribution lets you price AI features sustainably.
AWS, Azure, GCP, OpenAI, Anthropic, open-weight - provider-agnostic.
AI economics that survive the next CFO review.
Two weeks to visibility. Eight to material savings. Zero quality regressions.